All of the algorithms proposed in chapters  through  focus on the case where only a single content and style example are available at inference. Ultimately defining `style' using a single image is an artificial constraint that does not align well with peoples' typical usage of the word. `Style' is rarely defined by just one artwork, instead it's usually based on similarities within a body of work created by one, or even multiple, artists. Defining style using multiple images can help disentangle style from content [1]}, [2]} by revealing which visual features are consistently present, and which shift based on content. While methods which currently take advantage of this are tuned to a particular artist or a small number of similar artists, they produce compelling results, making a strong empirical case that style should be defined using multiple images. A challenge for future work is discovering methods which can efficiently and effectively make use of a variable number of novel style examples at inference. In addition, I believe that fully taking advantage of the style images available will require methods which explicitly leverage the semantics of artwork.
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